Vasileios Belagiannis

Prof. Dr. Vasileios Belagiannis

Professorship for Machine Learning in Signal Processing

Department of Electrical-Electronic-Communication Engineering
Chair of Multimedia Communications and Signal Processing

Room: Room 06.033
Cauerstr. 7
91058 Erlangen

Office hours

By arrangement.

Vasileios Belagiannis is Professor at the Faculty of Engineering of the Friedrich-Alexander-Universität Erlangen-Nürnberg. He holds a degree in engineering (Greece, 2009) from Democritus University of Thrace, Engineering School of Xanthi and M.Sc. in Computational Science and Engineering from TU München (Germany, 2011). He completed his doctoral studies at TU München (2015) and then continued as post-doctoral research assistant at the University of Oxford (Visual Geometry Group). Prior to joining Friedrich-Alexander-Universität Erlangen-Nürnberg, he spent time in industry, working at OSRAM, and then Ulm University and Otto von Guericke University Magdeburg.

An updated list of publications can be found on Google Scholar.

I am always looking for highly motivated students to undertake a PhD.

Recent News:

 

Ongoing third-party funded projects

  • Always-on Deep Neural Networks

    (Third Party Funds Single)

    Term: 1. March 2023 - 28. February 2026
    Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
    Abstract
    Computer vision contributes in creating visual priors as self-contained tasks or input to another system. In the context of autonomous navigation, the system can be a mobile agent that not only relies on the raw sensory inputs, but also on computer vision algorithms for understanding the environment. Recent studies on embodied agents show that an agent acts more accurately when visual priors such as semantic segmentation, depth estimation are provided next to the raw input data. Producing the visual priors though comes at the cost of data collection and annotation. The latest approaches build on deep neural networks, which are trained with supervision. For that propose, a large pool of data and annotations has to be created prior to training the model. To address this limitation, simulation is an alternative source for data and annotation generation. In the context of deep neural networks, it can be considered for the replacing the real-world, where a large amount of synthetic data is created according to the task in place. Although, the data simulation has clear advantages over the real-world datasets, there is also a clear limitation. Training a deep neural network with synthetic data does not result in good performance on real-world data.In this research project, we are going to conduct research on closing the performance drop when transferring deep neural network models from the simulation to real-world applications. Our testbed for measuring the performance will be semantic image segmentation and depth estimation from a single image. In our research, we will propose algorithms that teach a deep neural network how to fast learn adapting into new environments. This concept is widely known as meta-learning. In this project, it will be explored for learning a model in simulation and then transferring it to the real-world. Meta-learning has never been seen as a way to tackle model transfer, but its formulation suits well to the problem.

  • Transfer von tiefen neuronalen Netzen von der Simulation in die reale Welt

    (Third Party Funds Single)

    Term: 1. December 2022 - 30. November 2025
    Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)

Teaching

Lectures

  • Machine Learning in Signal Processing
    • Further information is available on StudOn and Campo.
  • Introduction to Deep Learning
    • Further information is available on StudOn and Campo.
  • Advanced Topics in Deep Learning
    • Further information is available on StudOn and Campo.

Guide to scientific work

  • Guide to scientific work
    • Further information is available on StudOn and Campo.

Seminars

  • Seminar on Selected Topics in Multimedia Communications and Signal Processing
    • Further information is available on StudOn and Campo.
  • Seminar über Bachelor- und Masterarbeiten
    • Further information is available on StudOn and Campo.

Lab Courses

  • Machine Learning in Signal Processing
    • Further information is available on StudOn and Campo.

Publications

2023

2022

2021

2020

2019

2018

2017

  • , :
    Recurrent Human Pose Estimation
    12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017 (Washington, DC, 30. May 2017 - 3. June 2017)
    In: Proceedings - 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017 - 1st International Workshop on Adaptive Shot Learning for Gesture Understanding and Production, ASL4GUP 2017, Biometrics in the Wild, Bwild 2017, Heterogeneous Face Recognition, HFR 2017, Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation, DCER and HPE 2017 and 3rd Facial Expression Recognition and Analysis Challenge, FERA 2017
    DOI: 10.1109/FG.2017.64
    BibTeX: Download
  • , , , , , , , :
    Preface DLMIA 2017
    In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - 3rd International Workshop, DLMIA 2017 and 7th International Workshop, ML-CDS 2017 Held in Conjunction with MICCAI 2017, Proceedings, Springer Verlag, (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol.10553 LNCS)
    BibTeX: Download

2016

2015

2014

2012

2009

  • , , , , , :
    The vision system of the ACROBOTER project
    2nd International Conference on Intelligent Robotics and Applications, ICIRA 2009 (SGP, 16. December 2009 - 18. December 2009)
    In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    DOI: 10.1007/978-3-642-10817-4_94
    BibTeX: Download